Overview

Dataset statistics

Number of variables10
Number of observations345
Missing cells77
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.4 KiB
Average record size in memory220.8 B

Variable types

Numeric8
Categorical2

Dataset

DescriptionTest
AuthorTest
URL
Copyright(c) Test 2026

Alerts

Telecom Subscribers has constant value ""Constant
Total is highly overall correlated with Wireless and 4 other fieldsHigh correlation
Wireline is highly overall correlated with Urban and 1 other fieldsHigh correlation
Wireless is highly overall correlated with Total and 4 other fieldsHigh correlation
Rural is highly overall correlated with Total and 4 other fieldsHigh correlation
Urban is highly overall correlated with Total and 5 other fieldsHigh correlation
Public is highly overall correlated with Total and 5 other fieldsHigh correlation
Private is highly overall correlated with Total and 4 other fieldsHigh correlation
Total has 11 (3.2%) missing valuesMissing
Wireline has 11 (3.2%) missing valuesMissing
Wireless has 11 (3.2%) missing valuesMissing
Rural has 11 (3.2%) missing valuesMissing
Urban has 11 (3.2%) missing valuesMissing
Public has 11 (3.2%) missing valuesMissing
Private has 11 (3.2%) missing valuesMissing
State/overall is uniformly distributedUniform
Rural has 7 (2.0%) zerosZeros

Reproduction

Analysis started2023-06-25 02:02:46.427579
Analysis finished2023-06-25 02:03:34.646891
Duration48.22 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

At the end of March
Real number (ℝ)

Distinct15
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015
Minimum2008
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:34.954197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12011
median2015
Q32019
95-th percentile2022
Maximum2022
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.326769
Coefficient of variation (CV)0.0021472799
Kurtosis-1.2108507
Mean2015
Median Absolute Deviation (MAD)4
Skewness0
Sum695175
Variance18.72093
MonotonicityNot monotonic
2023-06-25T07:33:35.602751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2008 23
 
6.7%
2009 23
 
6.7%
2010 23
 
6.7%
2011 23
 
6.7%
2012 23
 
6.7%
2013 23
 
6.7%
2014 23
 
6.7%
2015 23
 
6.7%
2016 23
 
6.7%
2017 23
 
6.7%
Other values (5) 115
33.3%
ValueCountFrequency (%)
2008 23
6.7%
2009 23
6.7%
2010 23
6.7%
2011 23
6.7%
2012 23
6.7%
2013 23
6.7%
2014 23
6.7%
2015 23
6.7%
2016 23
6.7%
2017 23
6.7%
ValueCountFrequency (%)
2022 23
6.7%
2021 23
6.7%
2020 23
6.7%
2019 23
6.7%
2018 23
6.7%
2017 23
6.7%
2016 23
6.7%
2015 23
6.7%
2014 23
6.7%
2013 23
6.7%

Telecom Subscribers
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size30.1 KiB
Telecom Subscribers (In Million)
345 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters11040
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTelecom Subscribers (In Million)
2nd rowTelecom Subscribers (In Million)
3rd rowTelecom Subscribers (In Million)
4th rowTelecom Subscribers (In Million)
5th rowTelecom Subscribers (In Million)

Common Values

ValueCountFrequency (%)
Telecom Subscribers (In Million) 345
100.0%

Length

2023-06-25T07:33:36.295318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T07:33:37.053223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
telecom 345
25.0%
subscribers 345
25.0%
in 345
25.0%
million 345
25.0%

Most occurring characters

ValueCountFrequency (%)
l 1035
 
9.4%
1035
 
9.4%
e 1035
 
9.4%
i 1035
 
9.4%
b 690
 
6.2%
c 690
 
6.2%
o 690
 
6.2%
n 690
 
6.2%
s 690
 
6.2%
r 690
 
6.2%
Other values (8) 2760
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7935
71.9%
Uppercase Letter 1380
 
12.5%
Space Separator 1035
 
9.4%
Open Punctuation 345
 
3.1%
Close Punctuation 345
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1035
13.0%
e 1035
13.0%
i 1035
13.0%
b 690
8.7%
c 690
8.7%
o 690
8.7%
n 690
8.7%
s 690
8.7%
r 690
8.7%
u 345
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
M 345
25.0%
I 345
25.0%
T 345
25.0%
S 345
25.0%
Space Separator
ValueCountFrequency (%)
1035
100.0%
Open Punctuation
ValueCountFrequency (%)
( 345
100.0%
Close Punctuation
ValueCountFrequency (%)
) 345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9315
84.4%
Common 1725
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1035
11.1%
e 1035
11.1%
i 1035
11.1%
b 690
 
7.4%
c 690
 
7.4%
o 690
 
7.4%
n 690
 
7.4%
s 690
 
7.4%
r 690
 
7.4%
M 345
 
3.7%
Other values (5) 1725
18.5%
Common
ValueCountFrequency (%)
1035
60.0%
( 345
 
20.0%
) 345
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1035
 
9.4%
1035
 
9.4%
e 1035
 
9.4%
i 1035
 
9.4%
b 690
 
6.2%
c 690
 
6.2%
o 690
 
6.2%
n 690
 
6.2%
s 690
 
6.2%
r 690
 
6.2%
Other values (8) 2760
25.0%

State/overall
Categorical

UNIFORM 

Distinct23
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Andhra Pradesh
 
15
Assam
 
15
Bihar
 
15
Gujarat
 
15
Haryana
 
15
Other values (18)
270 

Length

Max length39
Median length14
Mean length10.434783
Min length5

Characters and Unicode

Total characters3600
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndhra Pradesh
2nd rowAndhra Pradesh
3rd rowAndhra Pradesh
4th rowAndhra Pradesh
5th rowAndhra Pradesh

Common Values

ValueCountFrequency (%)
Andhra Pradesh 15
 
4.3%
Assam 15
 
4.3%
Bihar 15
 
4.3%
Gujarat 15
 
4.3%
Haryana 15
 
4.3%
Himachal Pradesh 15
 
4.3%
Jammu and Kashmir 15
 
4.3%
Karnataka 15
 
4.3%
Kerala 15
 
4.3%
Madhya Pradesh 15
 
4.3%
Other values (13) 195
56.5%

Length

2023-06-25T07:33:37.758109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 60
 
11.1%
chennai 15
 
2.8%
tamil 15
 
2.8%
nadu 15
 
2.8%
uttar 15
 
2.8%
west 15
 
2.8%
bengal 15
 
2.8%
kolkata 15
 
2.8%
andhra 15
 
2.8%
rajasthan 15
 
2.8%
Other values (23) 345
63.9%

Most occurring characters

ValueCountFrequency (%)
a 675
18.8%
r 300
 
8.3%
h 240
 
6.7%
s 225
 
6.2%
e 225
 
6.2%
195
 
5.4%
i 165
 
4.6%
t 150
 
4.2%
n 135
 
3.8%
d 135
 
3.8%
Other values (31) 1155
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2865
79.6%
Uppercase Letter 510
 
14.2%
Space Separator 195
 
5.4%
Dash Punctuation 30
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 675
23.6%
r 300
10.5%
h 240
 
8.4%
s 225
 
7.9%
e 225
 
7.9%
i 165
 
5.8%
t 150
 
5.2%
n 135
 
4.7%
d 135
 
4.7%
l 120
 
4.2%
Other values (11) 495
17.3%
Uppercase Letter
ValueCountFrequency (%)
P 75
14.7%
K 60
11.8%
M 45
 
8.8%
A 45
 
8.8%
S 30
 
5.9%
H 30
 
5.9%
O 30
 
5.9%
B 30
 
5.9%
N 30
 
5.9%
E 15
 
2.9%
Other values (8) 120
23.5%
Space Separator
ValueCountFrequency (%)
195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3375
93.8%
Common 225
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 675
20.0%
r 300
 
8.9%
h 240
 
7.1%
s 225
 
6.7%
e 225
 
6.7%
i 165
 
4.9%
t 150
 
4.4%
n 135
 
4.0%
d 135
 
4.0%
l 120
 
3.6%
Other values (29) 1005
29.8%
Common
ValueCountFrequency (%)
195
86.7%
- 30
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 675
18.8%
r 300
 
8.3%
h 240
 
6.7%
s 225
 
6.2%
e 225
 
6.2%
195
 
5.4%
i 165
 
4.6%
t 150
 
4.2%
n 135
 
3.8%
d 135
 
3.8%
Other values (31) 1155
32.1%

Total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct326
Distinct (%)97.6%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean84.852365
Minimum2.46
Maximum1211.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:38.628859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.46
5-th percentile7.3665
Q121.7125
median39.4
Q368.69
95-th percentile166.736
Maximum1211.8
Range1209.34
Interquartile range (IQR)46.9775

Descriptive statistics

Standard deviation198.5168
Coefficient of variation (CV)2.3395553
Kurtosis21.544076
Mean84.852365
Median Absolute Deviation (MAD)23.795
Skewness4.6971896
Sum28340.69
Variance39408.921
MonotonicityNot monotonic
2023-06-25T07:33:40.478341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.63 3
 
0.9%
2.46 2
 
0.6%
69.19 2
 
0.6%
56.58 2
 
0.6%
9.33 2
 
0.6%
73.2 2
 
0.6%
9.06 2
 
0.6%
63.88 1
 
0.3%
80.87 1
 
0.3%
58.71 1
 
0.3%
Other values (316) 316
91.6%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
2.46 2
0.6%
2.72 1
0.3%
3.69 1
0.3%
3.7 1
0.3%
3.74 1
0.3%
4.34 1
0.3%
5.34 1
0.3%
5.64 1
0.3%
5.78 1
0.3%
5.95 1
0.3%
ValueCountFrequency (%)
1211.8 1
0.3%
1200.88 1
0.3%
1194.99 1
0.3%
1183.41 1
0.3%
1176.79 1
0.3%
1166.84 1
0.3%
1059.33 1
0.3%
996.13 1
0.3%
951.35 1
0.3%
933.02 1
0.3%

Wireline
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct214
Distinct (%)64.1%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean2.5439521
Minimum0.09
Maximum39.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:41.336703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.13
Q10.37
median1.17
Q32.32
95-th percentile3.6505
Maximum39.41
Range39.32
Interquartile range (IQR)1.95

Descriptive statistics

Standard deviation5.8483999
Coefficient of variation (CV)2.2989426
Kurtosis21.175389
Mean2.5439521
Median Absolute Deviation (MAD)0.89
Skewness4.594297
Sum849.68
Variance34.203782
MonotonicityNot monotonic
2023-06-25T07:33:42.164221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23 5
 
1.4%
0.13 5
 
1.4%
0.28 5
 
1.4%
0.12 5
 
1.4%
0.1 5
 
1.4%
0.17 4
 
1.2%
0.31 4
 
1.2%
0.16 4
 
1.2%
3.06 4
 
1.2%
1.38 4
 
1.2%
Other values (204) 289
83.8%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
0.09 2
 
0.6%
0.1 5
1.4%
0.11 3
0.9%
0.12 5
1.4%
0.13 5
1.4%
0.14 2
 
0.6%
0.15 2
 
0.6%
0.16 4
1.2%
0.17 4
1.2%
0.18 1
 
0.3%
ValueCountFrequency (%)
39.41 1
0.3%
37.96 1
0.3%
36.96 1
0.3%
34.73 1
0.3%
32.17 1
0.3%
30.21 1
0.3%
28.5 1
0.3%
26.59 1
0.3%
25.22 1
0.3%
24.82 1
0.3%

Wireless
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct330
Distinct (%)98.8%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean82.308323
Minimum2.12
Maximum1188.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:43.296222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.12
5-th percentile7.046
Q120.1725
median36.98
Q367.1125
95-th percentile165.705
Maximum1188.99
Range1186.87
Interquartile range (IQR)46.94

Descriptive statistics

Standard deviation193.61961
Coefficient of variation (CV)2.3523698
Kurtosis21.867087
Mean82.308323
Median Absolute Deviation (MAD)23.19
Skewness4.7255762
Sum27490.98
Variance37488.554
MonotonicityNot monotonic
2023-06-25T07:33:44.163314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.45 2
 
0.6%
22.56 2
 
0.6%
31.35 2
 
0.6%
16.4 2
 
0.6%
64.24 1
 
0.3%
42.34 1
 
0.3%
27.78 1
 
0.3%
18.28 1
 
0.3%
63.19 1
 
0.3%
66.3 1
 
0.3%
Other values (320) 320
92.8%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
2.12 1
0.3%
2.2 1
0.3%
2.3 1
0.3%
3.32 1
0.3%
3.35 1
0.3%
3.5 1
0.3%
3.91 1
0.3%
4.99 1
0.3%
5.18 1
0.3%
5.31 1
0.3%
ValueCountFrequency (%)
1188.99 1
0.3%
1180.64 1
0.3%
1170.59 1
0.3%
1161.71 1
0.3%
1157.67 1
0.3%
1142.02 1
0.3%
1034.11 1
0.3%
969.54 1
0.3%
919.17 1
0.3%
904.52 1
0.3%

Rural
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct312
Distinct (%)93.4%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean34.279671
Minimum0
Maximum537.11
Zeros7
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:45.107980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.706
Q15.01
median13.795
Q328.53
95-th percentile86.741
Maximum537.11
Range537.11
Interquartile range (IQR)23.52

Descriptive statistics

Standard deviation83.467433
Coefficient of variation (CV)2.434896
Kurtosis23.538236
Mean34.279671
Median Absolute Deviation (MAD)10.78
Skewness4.8320813
Sum11449.41
Variance6966.8124
MonotonicityNot monotonic
2023-06-25T07:33:46.117539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
2.0%
0.12 3
 
0.9%
34.38 3
 
0.9%
34.57 2
 
0.6%
22.51 2
 
0.6%
2.37 2
 
0.6%
34.84 2
 
0.6%
1.12 2
 
0.6%
6.23 2
 
0.6%
4.68 2
 
0.6%
Other values (302) 307
89.0%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
0 7
2.0%
0.11 1
 
0.3%
0.12 3
0.9%
0.13 1
 
0.3%
0.34 1
 
0.3%
0.35 1
 
0.3%
0.53 1
 
0.3%
0.65 1
 
0.3%
0.68 1
 
0.3%
0.72 1
 
0.3%
ValueCountFrequency (%)
537.11 1
0.3%
525.87 1
0.3%
521.26 1
0.3%
519.63 1
0.3%
514.27 1
0.3%
501.81 1
0.3%
447.77 1
0.3%
416.08 1
0.3%
377.78 1
0.3%
349.21 1
0.3%

Urban
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct323
Distinct (%)96.7%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean50.573174
Minimum0.9
Maximum693.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:47.004398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile3.4865
Q111.6825
median24.485
Q341.45
95-th percentile79.659
Maximum693.18
Range692.28
Interquartile range (IQR)29.7675

Descriptive statistics

Standard deviation116.37407
Coefficient of variation (CV)2.3011027
Kurtosis20.068847
Mean50.573174
Median Absolute Deviation (MAD)14.685
Skewness4.5666122
Sum16891.44
Variance13542.924
MonotonicityNot monotonic
2023-06-25T07:33:47.791028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.59 2
 
0.6%
19.28 2
 
0.6%
9.07 2
 
0.6%
15.65 2
 
0.6%
30.32 2
 
0.6%
43.59 2
 
0.6%
16.32 2
 
0.6%
39.29 2
 
0.6%
24.95 2
 
0.6%
29.5 2
 
0.6%
Other values (313) 314
91.0%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
0.9 1
0.3%
1.29 1
0.3%
1.81 1
0.3%
1.93 1
0.3%
2.19 1
0.3%
2.34 1
0.3%
2.61 1
0.3%
2.72 1
0.3%
2.73 1
0.3%
2.78 1
0.3%
ValueCountFrequency (%)
693.18 1
0.3%
685.93 1
0.3%
669.14 1
0.3%
663.76 1
0.3%
655.54 1
0.3%
647.21 1
0.3%
620.52 1
0.3%
611.56 1
0.3%
580.05 1
0.3%
564.04 1
0.3%

Public
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct284
Distinct (%)85.0%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean10.604611
Minimum1.05
Maximum134.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:48.654910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile1.413
Q12.88
median4.89
Q37.8775
95-th percentile18.2095
Maximum134.97
Range133.92
Interquartile range (IQR)4.9975

Descriptive statistics

Standard deviation23.889614
Coefficient of variation (CV)2.2527573
Kurtosis18.227276
Mean10.604611
Median Absolute Deviation (MAD)2.235
Skewness4.3885297
Sum3541.94
Variance570.71368
MonotonicityNot monotonic
2023-06-25T07:33:49.595483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.05 3
 
0.9%
3.09 3
 
0.9%
1.43 3
 
0.9%
1.71 3
 
0.9%
5.78 2
 
0.6%
5.69 2
 
0.6%
5.13 2
 
0.6%
6.08 2
 
0.6%
6.85 2
 
0.6%
1.63 2
 
0.6%
Other values (274) 310
89.9%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
1.05 3
0.9%
1.1 1
 
0.3%
1.17 1
 
0.3%
1.18 1
 
0.3%
1.21 1
 
0.3%
1.25 1
 
0.3%
1.27 1
 
0.3%
1.32 1
 
0.3%
1.34 1
 
0.3%
1.35 1
 
0.3%
ValueCountFrequency (%)
134.97 1
0.3%
133.51 1
0.3%
131.66 1
0.3%
131.16 1
0.3%
130.27 1
0.3%
130.11 1
0.3%
127.1 1
0.3%
126 1
0.3%
122.18 1
0.3%
120.05 1
0.3%

Private
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct328
Distinct (%)98.2%
Missing11
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean74.247994
Minimum1.28
Maximum1080.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-06-25T07:33:50.519919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile5.641
Q117.4375
median33.08
Q361.3775
95-th percentile149.0565
Maximum1080.14
Range1078.86
Interquartile range (IQR)43.94

Descriptive statistics

Standard deviation175.28951
Coefficient of variation (CV)2.3608653
Kurtosis22.096124
Mean74.247994
Median Absolute Deviation (MAD)21.69
Skewness4.7453573
Sum24798.83
Variance30726.413
MonotonicityNot monotonic
2023-06-25T07:33:51.423031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9 2
 
0.6%
6.44 2
 
0.6%
5.69 2
 
0.6%
10.6 2
 
0.6%
12.29 2
 
0.6%
47.75 2
 
0.6%
59.35 1
 
0.3%
24.47 1
 
0.3%
15.64 1
 
0.3%
57.04 1
 
0.3%
Other values (318) 318
92.2%
(Missing) 11
 
3.2%
ValueCountFrequency (%)
1.28 1
0.3%
1.41 1
0.3%
1.67 1
0.3%
2.43 1
0.3%
2.57 1
0.3%
2.58 1
0.3%
2.99 1
0.3%
3.71 1
0.3%
4.01 1
0.3%
4.24 1
0.3%
ValueCountFrequency (%)
1080.14 1
0.3%
1072.81 1
0.3%
1069.72 1
0.3%
1049.9 1
0.3%
1041.82 1
0.3%
1039.74 1
0.3%
950.68 1
0.3%
895.79 1
0.3%
821.08 1
0.3%
812.96 1
0.3%

Interactions

2023-06-25T07:33:26.668811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:48.545543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:53.348033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:58.026336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:06.432933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:11.051293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:16.389822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:21.554779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:27.285091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:49.351630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:53.962604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:58.693786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:07.029435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:11.653569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:17.148017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:22.327893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:27.855476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:50.049615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:54.505795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:59.284384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:07.526757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:12.220038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:17.796961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:22.958895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:28.575513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:50.589368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:55.159685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:59.943502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:08.195905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:12.883008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:18.466206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:23.650964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:29.280301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:51.051398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:55.709114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:00.529550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:08.767443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:13.655895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:19.032484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:24.265486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:29.944451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:51.581069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:56.317061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:01.148796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:09.286687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:14.263778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:19.592626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:24.852945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:30.491823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:52.171150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:56.999750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:05.196728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:09.832693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:14.997622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:20.229248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:25.448829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:31.142044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:52.837208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:32:57.564898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:05.811986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:10.480922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:15.757175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:20.869308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-25T07:33:26.057269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-25T07:33:52.094701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
At the end of MarchTotalWirelineWirelessRuralUrbanPublicPrivateState/overall
At the end of March1.0000.386-0.2560.4040.3800.2920.1440.4050.000
Total0.3861.0000.4820.9990.8260.9330.8180.9970.391
Wireline-0.2560.4821.0000.4540.0850.6690.6040.4580.320
Wireless0.4040.9990.4541.0000.8380.9250.8110.9970.391
Rural0.3800.8260.0850.8381.0000.5920.7220.8160.340
Urban0.2920.9330.6690.9250.5921.0000.7820.9320.393
Public0.1440.8180.6040.8110.7220.7821.0000.7800.462
Private0.4050.9970.4580.9970.8160.9320.7801.0000.380
State/overall0.0000.3910.3200.3910.3400.3930.4620.3801.000

Missing values

2023-06-25T07:33:31.978848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-25T07:33:32.899424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-25T07:33:34.028530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

At the end of MarchTelecom SubscribersState/overallTotalWirelineWirelessRuralUrbanPublicPrivate
02008Telecom Subscribers (In Million)Andhra Pradesh23.292.7120.586.2317.064.9318.36
12009Telecom Subscribers (In Million)Andhra Pradesh32.952.5530.409.1723.785.6627.29
22010Telecom Subscribers (In Million)Andhra Pradesh48.092.4645.6214.7833.306.6241.46
32011Telecom Subscribers (In Million)Andhra Pradesh63.052.3760.6820.6642.399.3153.74
42012Telecom Subscribers (In Million)Andhra Pradesh69.192.3666.8324.2344.9510.9958.20
52013Telecom Subscribers (In Million)Andhra Pradesh66.602.2464.3626.0640.5511.2355.37
62014Telecom Subscribers (In Million)Andhra Pradesh69.192.0467.1528.5840.6111.5857.62
72015Telecom Subscribers (In Million)Andhra Pradesh73.821.8771.9531.0242.8011.1062.72
82016Telecom Subscribers (In Million)Andhra Pradesh76.391.7474.6532.7343.6610.9965.39
92017Telecom Subscribers (In Million)Andhra Pradesh86.581.6484.9438.7347.8511.1475.44
At the end of MarchTelecom SubscribersState/overallTotalWirelineWirelessRuralUrbanPublicPrivate
3352013Telecom Subscribers (In Million)Service Area wise Subscribers - Overall898.0230.21867.81349.21548.80130.11767.91
3362014Telecom Subscribers (In Million)Service Area wise Subscribers - Overall933.0228.50904.52377.78555.23120.05812.96
3372015Telecom Subscribers (In Million)Service Area wise Subscribers - Overall996.1326.59969.54416.08580.05100.34895.79
3382016Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1059.3325.221034.11447.77611.56108.65950.68
3392017Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1194.9924.401170.59501.81693.18122.181072.81
3402018Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1211.8022.811188.99525.87685.93131.661080.14
3412019Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1183.4121.701161.71514.27669.14133.511049.90
3422020Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1176.7919.131157.67521.26655.54134.971041.82
3432021Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1200.8820.241180.64537.11663.76131.161069.72
3442022Telecom Subscribers (In Million)Service Area wise Subscribers - Overall1166.8424.821142.02519.63647.21127.101039.74